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Zhipeng Tang
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

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Journal article
Published: 30 July 2021 in Energies
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The carbon intensity of China’s resource-based cities (RBCs) is much higher than the national average due to their relatively intensive mode of development. Low carbon transformation of RBCs is an important way to achieve the goal of reaching the carbon emissions peak in 2030. Based on the panel data from 116 RBCs in China from 2003 to 2018, this study takes the opening of high-speed railway (HSR) lines as a quasi-experiment, using a time-varying difference-in-difference (DID) model to empirically evaluate the impact of an HSR line on reducing the carbon intensity of RBCs. The results show that the opening of an HSR line can reduce the carbon intensity of RBCs, and this was still true after considering the possibility of problems with endogenous selection bias and after applying the relevant robustness tests. The opening of an HSR line is found to have a significant reducing effect on the carbon intensity of different types of RBC, and the decline in the carbon intensity of coal-based cities is found to be the greatest. Promoting migration of RBCs with HSR lines is found to be an effective intermediary way of reducing their carbon intensity.

ACS Style

Zhipeng Tang; Ziao Mei; Jialing Zou. Does the Opening of High-Speed Railway Lines Reduce the Carbon Intensity of China’s Resource-Based Cities? Energies 2021, 14, 4648 .

AMA Style

Zhipeng Tang, Ziao Mei, Jialing Zou. Does the Opening of High-Speed Railway Lines Reduce the Carbon Intensity of China’s Resource-Based Cities? Energies. 2021; 14 (15):4648.

Chicago/Turabian Style

Zhipeng Tang; Ziao Mei; Jialing Zou. 2021. "Does the Opening of High-Speed Railway Lines Reduce the Carbon Intensity of China’s Resource-Based Cities?" Energies 14, no. 15: 4648.

Journal article
Published: 07 June 2021 in Sustainability
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Coal regulation has been implemented throughout China. However, the potential benefits of pollution abatement and the co-benefits of residents’ health were rarely assessed. In this study, based on the analysis of historical coal consumption and multiple coal regulation measures in Anhui Province, China, four scenarios (Business as Usual (BU), Structure Optimization (SO), Gross Consumption Control (GC), and Comprehensive Measures (CM)) were constructed to indicate four different paths from 2020 to 2060, which is a vital period for realizing carbon neutrality. The results show that reductions of SO2, PM10, and PM2.5 emissions in the SO scenario are higher than those in the GC scenario, while the reduction of NOx emission is higher in the GC scenario. Compared with the BU scenario, residents’ health benefits from 2020 to 2060 are 8.3, 4.8, and 4.5 billion USD in the CM, GC, and SO scenarios, respectively, indicating that the achievements of coal regulation are significant for health promotion. Therefore, the optimization and implementation of coal regulation in the future is not only essential for the carbon neutrality target, but also a significant method to yield environmental and health co-benefits.

ACS Style

Wu Xie; Wenzhe Guo; Wenbin Shao; Fangyi Li; Zhipeng Tang. Environmental and Health Co-Benefits of Coal Regulation under the Carbon Neutral Target: A Case Study in Anhui Province, China. Sustainability 2021, 13, 6498 .

AMA Style

Wu Xie, Wenzhe Guo, Wenbin Shao, Fangyi Li, Zhipeng Tang. Environmental and Health Co-Benefits of Coal Regulation under the Carbon Neutral Target: A Case Study in Anhui Province, China. Sustainability. 2021; 13 (11):6498.

Chicago/Turabian Style

Wu Xie; Wenzhe Guo; Wenbin Shao; Fangyi Li; Zhipeng Tang. 2021. "Environmental and Health Co-Benefits of Coal Regulation under the Carbon Neutral Target: A Case Study in Anhui Province, China." Sustainability 13, no. 11: 6498.

Journal article
Published: 06 January 2021 in Sustainable Cities and Society
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Globally, urbanization has dramatically changed land cover, causing a rapid growth in carbon emissions and related risk of climate change. This study estimated city-level land use carbon emissions (LUCEs) using a novel method based on the correction coefficient calculated by the carbon emissions from energy consumption and basic land use emissions. The method was applied to 13 cities in the Beijing-Tianjin-Hebei (BTH) urban agglomeration in China using 30 m resolution land use data and energy balance tables (EBTs), and the Environmental Kuznets Curve (EKC) was used to discuss the relationship between urbanization and LUCEs in three typical models. The results revealed the expansion of built-up land in the BTH region, and LUCEs at the city-level increase continually, except in Beijing, which showed the most significant expansion of built-up land but a declining trend in LUCEs in recent years. The relationship between urbanization and LUCEs can be summarized into three modes: ‘high urbanization - low emissions’, ‘middle urbanization - high emissions’, and ‘low urbanization - low emissions’. The results have great significance for the formulation of policies to reduce city-level carbon emission at different urbanization levels, and the implementation of high-quality people-oriented new-type urbanization can allow the realization of the carbon emission reduction targets of China.

ACS Style

Yuan Zhou; Mingxing Chen; Zhipeng Tang; Ziao Mei. Urbanization, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region. Sustainable Cities and Society 2021, 66, 102701 .

AMA Style

Yuan Zhou, Mingxing Chen, Zhipeng Tang, Ziao Mei. Urbanization, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region. Sustainable Cities and Society. 2021; 66 ():102701.

Chicago/Turabian Style

Yuan Zhou; Mingxing Chen; Zhipeng Tang; Ziao Mei. 2021. "Urbanization, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region." Sustainable Cities and Society 66, no. : 102701.

Regional graphic
Published: 01 January 2021 in Regional Studies, Regional Science
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In response to the outbreak of COVID-19, the Chinese government imposed stringent lockdown measures to minimize the spread of the disease. This paper shows that under these measures, the PM2.5 concentrations were lower in February 2020 than in February 2019. However, after the measures were removed, the PM2.5 concentration returned to the same level as in the previous year, thus implicating that the reduction was temporary.

ACS Style

Zhipeng Tang; Ziao Mei; Guangjun Sui; Jialing Zou. Visualizing the impact of COVID-19 on PM2.5 concentrations in China. Regional Studies, Regional Science 2021, 8, 51 -53.

AMA Style

Zhipeng Tang, Ziao Mei, Guangjun Sui, Jialing Zou. Visualizing the impact of COVID-19 on PM2.5 concentrations in China. Regional Studies, Regional Science. 2021; 8 (1):51-53.

Chicago/Turabian Style

Zhipeng Tang; Ziao Mei; Guangjun Sui; Jialing Zou. 2021. "Visualizing the impact of COVID-19 on PM2.5 concentrations in China." Regional Studies, Regional Science 8, no. 1: 51-53.

Journal article
Published: 16 July 2020 in Journal of Cleaner Production
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Reductions of export-driven CO2 and air pollutants emissions are crucial to promote green transition of exports and realize sustainable development goals for developing countries and regions. This study aims to explore the coordinated effects (co-effects) on export-driven emissions (EEs) and the hidden driving forces in China’s individual provinces. Based on the multi-regional input-output (MRIO) tables and structural decomposition analysis (SDA) method, seven socioeconomic factors of the changes in CO2 and particulate matter (PM) EEs during 2007-2012 are estimated by province. The co-effects on provincial EEs, categorized as co-benefits, trade-offs and co-detriments, are assessed to reveal regional disparities. The results show that the changes in energy efficiency have led to the greatest co-benefits in all factors, while changes in emission coefficients, industrial structure, and regional distribution of exports have led to limited co-benefits in a few of provinces. The other factors, variations of population and export per capita, have resulted in co-detriments. The co-effects and their factors varied greatly across provinces. Some provinces were more likely to obtain co-benefits because of their first mover advantages or special actions for green development, such as Hebei, Henan, Hunan, Shandong, Tianjin, Yunnan and Guizhou, dispersedly distributed in coastal and inland regions. On the other hand, as trade-offs and co-detriments existed extensively in some provinces, related factors should be monitored and adjusted. Identifying co-effects and corresponding key factors at the regional level provides valuable insights into green transition of China’s exports, and raises the importance of policy integration and regional cooperation.

ACS Style

Wenbin Shao; Fangyi Li; Xin Cao; Zhipeng Tang; Yu Bai; Shanlin Yang. Reducing export-driven CO2 and PM emissions in China’s provinces: A structural decomposition and coordinated effects analysis. Journal of Cleaner Production 2020, 274, 123101 .

AMA Style

Wenbin Shao, Fangyi Li, Xin Cao, Zhipeng Tang, Yu Bai, Shanlin Yang. Reducing export-driven CO2 and PM emissions in China’s provinces: A structural decomposition and coordinated effects analysis. Journal of Cleaner Production. 2020; 274 ():123101.

Chicago/Turabian Style

Wenbin Shao; Fangyi Li; Xin Cao; Zhipeng Tang; Yu Bai; Shanlin Yang. 2020. "Reducing export-driven CO2 and PM emissions in China’s provinces: A structural decomposition and coordinated effects analysis." Journal of Cleaner Production 274, no. : 123101.

Article
Published: 29 April 2020 in Journal of Geographical Sciences
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The Chinese government ratified the Paris Climate Agreement in 2016. Accordingly, China aims to reduce carbon dioxide emissions per unit of gross domestic product (carbon intensity) to 60%–65% of 2005 levels by 2030. However, since numerous factors influence carbon intensity in China, it is critical to assess their relative importance to determine the most important factors. As traditional methods are inadequate for identifying key factors from a range of factors acting in concert, machine learning was applied in this study. Specifically, random forest algorithm, which is based on decision tree theory, was employed because it is insensitive to multicollinearity, is robust to missing and unbalanced data, and provides reasonable predictive results. We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017. The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry, the proportion of fossil fuel-based energy, and technological progress. The Chinese economy developed rapidly between 1992 and 2007; during this time, the effects of the proportion of service industry, price of fossil fuel, and traditional residential consumption on carbon intensity increased. Subsequently, the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis; during this period, reductions in emissions and the availability of new energy types began to have effects on carbon intensity, and the importance of residential consumption increased. The results suggest that optimizing the energy and industrial structures, promoting technological advancement, increasing green consumption, and reducing emissions are keys to decreasing carbon intensity within China in the future. These approaches will help achieve the goal of reducing carbon intensity to 60%–65% of the 2005 level by 2030.

ACS Style

Zhipeng Tang; Ziao Mei; Weidong Liu; Yan Xia. Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm. Journal of Geographical Sciences 2020, 30, 743 -756.

AMA Style

Zhipeng Tang, Ziao Mei, Weidong Liu, Yan Xia. Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm. Journal of Geographical Sciences. 2020; 30 (5):743-756.

Chicago/Turabian Style

Zhipeng Tang; Ziao Mei; Weidong Liu; Yan Xia. 2020. "Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm." Journal of Geographical Sciences 30, no. 5: 743-756.

Journal article
Published: 17 April 2020 in Sustainability
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Tibet in China has extremely a fragile natural ecosystem, which is under a great pressure from global changes. The carbon footprint (CF) and water footprint (WF), reflecting the pressures of regional development on the natural environment, represent a lacuna in the field of study in Tibet due to missing data. In this paper, the 2012 multi-regional input–output table of China was employed to quantify the CF and WF of Tibet and the relationship between Tibet and other provinces of China. Spatial pattern and key sectors were also studied to demonstrate the current characters and the future trend of footprints. Tibet’s carbon emission was 4.0 Mt, 32.7% of CF, indicating that Tibet was a net importing region of carbon emission. Tibet received embodied carbon emission by trade from other regions, especially from Hebei, Inner Mongolia and Henan provinces, but played a complex role in virtual water allocation by transferring to most provinces and receiving from some provinces. The CF of Tibet will increase under different scenarios of 2030, but the WF can be restricted to 2.5 Gt in the slow scenario. In the future, imports of virtual resources will benefit the fragile ecosystem of Tibet and moreover, it is vital to restrict the local resource-intensive sectors and improve resource-use efficiency.

ACS Style

Wu Xie; Shuai Hu; Fangyi Li; Xin Cao; Zhipeng Tang. Carbon and Water Footprints of Tibet: Spatial Pattern and Trend Analysis. Sustainability 2020, 12, 3294 .

AMA Style

Wu Xie, Shuai Hu, Fangyi Li, Xin Cao, Zhipeng Tang. Carbon and Water Footprints of Tibet: Spatial Pattern and Trend Analysis. Sustainability. 2020; 12 (8):3294.

Chicago/Turabian Style

Wu Xie; Shuai Hu; Fangyi Li; Xin Cao; Zhipeng Tang. 2020. "Carbon and Water Footprints of Tibet: Spatial Pattern and Trend Analysis." Sustainability 12, no. 8: 3294.

Journal article
Published: 09 September 2019 in Sustainability
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In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.

ACS Style

Jialing Zou; Zhipeng Tang; Shuang Wu. Divergent Leading Factors in Energy-Related CO2 Emissions Change Among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis. Sustainability 2019, 11, 4929 .

AMA Style

Jialing Zou, Zhipeng Tang, Shuang Wu. Divergent Leading Factors in Energy-Related CO2 Emissions Change Among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis. Sustainability. 2019; 11 (18):4929.

Chicago/Turabian Style

Jialing Zou; Zhipeng Tang; Shuang Wu. 2019. "Divergent Leading Factors in Energy-Related CO2 Emissions Change Among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis." Sustainability 11, no. 18: 4929.

Journal article
Published: 15 June 2019 in Journal of Cleaner Production
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As the headwaters region of many major Asian rivers and one of the most vulnerable ecosystems in the world, Tibet is an important strategic water resources and ecological conservation district in China. Although Tibet has developed increasing economic links with external regions, the virtual water (VW) interactions have rarely been discussed due to data unavailability. In this study, based on a newly released China multiple-regional input-output table in 2012, Tibet's local water use is analyzed by associating it with interregional-level trades in China. The structural decomposition analysis (SDA), which is conventionally applied to trace influencing factors for temporal changes of environmental variables, is adapted to examine socio-economic determinants of net VW trade in Tibet. The results indicate that Tibet is a net VW exporter, providing 577.5 million m3 net VW to other provinces. The SDA results provide quantified effects of different factors. Tibet's low direct water use intensity and water intensive dominated export structure contribute 1048.6 and −738.9 million m3 net VW exports, respectively. In contrast, the trade volume of commodities and production structure contribute to offsetting the net VW export 1140.7 and 69.4 million m3, respectively. Policies are informed towards sustainable water resources uses and management. Measures including resources use efficiency improvement, industry upgrade towards a less resource intensive production structure and material support from external regions are suggested, highlighting the importance of strengthening coordination between Tibet and downstream regions.

ACS Style

Siao Sun; Chao Bao; Zhipeng Tang. Tele-connecting water consumption in Tibet: Patterns and socio-economic driving factors for virtual water trades. Journal of Cleaner Production 2019, 233, 1250 -1258.

AMA Style

Siao Sun, Chao Bao, Zhipeng Tang. Tele-connecting water consumption in Tibet: Patterns and socio-economic driving factors for virtual water trades. Journal of Cleaner Production. 2019; 233 ():1250-1258.

Chicago/Turabian Style

Siao Sun; Chao Bao; Zhipeng Tang. 2019. "Tele-connecting water consumption in Tibet: Patterns and socio-economic driving factors for virtual water trades." Journal of Cleaner Production 233, no. : 1250-1258.

Journal article
Published: 04 August 2018 in Sustainability
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China’s economy has been highly reliant on exports in recent years, with Guangdong its biggest province in export trade volume. Despite the global financial crisis of 2008, exports from Guangdong continued to increase significantly; however, the energy consumption embodied in exports is unknown. In this study, we investigate the changes of energy embodied in exports from 2007 to 2012 in Guangdong Province. We use EIO (Environmental Input-Output) and LMDI (Logarithmic Mean Divisia Index) method to find out the drivers of such changes embodied in total exports and export of each sector. Our results show: Firstly, from 2007 to 2012, the export structure in Guangdong has changed, reflecting in low energy intensity industry experiencing faster growth in exports than high energy intensity industry. Secondly, the growth rate of embodied energy consumption in Guangdong’s exports is slowing, with average annual growth from 2007 to 2012 of 6.8%. Thirdly, though Guangdong’s exports grew significantly, the energy consumption embodied therein decreased by 23% from 2007 to 2012, representing a drop of 50.51 Mtce. Finally, the most prominent change driver differed across sectors: For low value-added industries, such as metal smelting and rolling, the main contributor was export structure change, whereas for high value-added industries, such as communications, computers, and other electronic equipment, the main contributor was technical change. Guangdong is playing a leading role in industrial upgrading in China, and this has made the embodied energy consumption decreased obviously in Guangdong. It will be interesting to further investigate the trends of embodied energy consumption of other provinces in China, as this would give us deeper understanding of Chinese resource and environment problems.

ACS Style

Zhipeng Tang; Jialing Zou; Shuang Wu. What Drove Changes in the Embodied Energy Consumption of Guangdong’s Exports from 2007–2012? Sustainability 2018, 10, 2755 .

AMA Style

Zhipeng Tang, Jialing Zou, Shuang Wu. What Drove Changes in the Embodied Energy Consumption of Guangdong’s Exports from 2007–2012? Sustainability. 2018; 10 (8):2755.

Chicago/Turabian Style

Zhipeng Tang; Jialing Zou; Shuang Wu. 2018. "What Drove Changes in the Embodied Energy Consumption of Guangdong’s Exports from 2007–2012?" Sustainability 10, no. 8: 2755.

Journal article
Published: 18 March 2017 in Sustainability
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The aim of this paper is to identify the correlations between energy consumption and the factors that control usage in the city of Tangshan. To do this, we first analyze the current status of Tangshan’s economic development and energy consumption, and then applied the logarithmic mean Divisia index to identify the factors affecting the changes in energy consumption of all sectors. The findings are summarized as follows: (1) secondary industry accounts for an extremely high percentage of industry in Tangshan city, much higher than the national average; from 2007 to 2012, the proportion of secondary industry increased in Tangshan city; (2) Tangshan’s energy consumption in 2013 was nearly twice that in 2005. Coal and coke coal consumption was responsible for 96.2% of total energy consumption in 2005 and 95.1% in 2013; (3) Tangshan’s energy intensity decreased from 3.00 tce/thousand Yuan in 2005 to 1.85 tce/thousand Yuan in 2013. However, the energy intensity of Tangshan was far more than the average for China, and the decline in Tangshan’s energy intensity was much slower than the average for China; (4) The technical effect plays a dominant role in decreasing energy consumption in most sectors, and the scale effect is the most important contributor to increasing energy consumption in all sectors. Input structural and final use structural effects play different roles in energy consumption in different sectors.

ACS Style

Jialing Zou; Weidong Liu; Zhipeng Tang. Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012. Sustainability 2017, 9, 452 .

AMA Style

Jialing Zou, Weidong Liu, Zhipeng Tang. Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012. Sustainability. 2017; 9 (3):452.

Chicago/Turabian Style

Jialing Zou; Weidong Liu; Zhipeng Tang. 2017. "Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012." Sustainability 9, no. 3: 452.

Journal article
Published: 21 August 2015 in Journal of Geographical Sciences
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Based on the multi-regional input-output analysis, this paper improves the four traditional input-output formulas about exports resulting in multi-regional carbon emissions spatial effects which include direct effect, indirect effect, spillover effect and feedback effect. And the latter two formulas are to measure the bidirectional influences of carbon emissions induced by regional exports between two regions. The results suggest that the direct effects of Chinese eight regions induced by national exports decreased from 1997 to 2010, and the indirect effects induced by national exports also decreased except the northern coastal region and the northwestern region in China. During this period, most of Chinese coastal regions had strong spillover effects induced by their own exports. The northern coastal region and the eastern coastal region had stronger feedback effects, while the southern coastal region had weaker feedback effects and Beijing-Tianjin region had the weakest feedback effect induced by their exports. All of the inland regions had strong feedback effects, especially for Northwest and Central China, induced by their exports. More attention should be paid to the inter-regional joint efforts in order to effectively achieve Chinese national carbon-reduction target.

ACS Style

Zhipeng Tang; Weidong Liu; Peiping Gong. The measurement of the spatial effects of Chinese regional carbon emissions caused by exports. Journal of Geographical Sciences 2015, 25, 1328 -1342.

AMA Style

Zhipeng Tang, Weidong Liu, Peiping Gong. The measurement of the spatial effects of Chinese regional carbon emissions caused by exports. Journal of Geographical Sciences. 2015; 25 (11):1328-1342.

Chicago/Turabian Style

Zhipeng Tang; Weidong Liu; Peiping Gong. 2015. "The measurement of the spatial effects of Chinese regional carbon emissions caused by exports." Journal of Geographical Sciences 25, no. 11: 1328-1342.